R Notebook

install.packages("sf")

Installing package into ‘/home/rstudio-user/R/x86_64-pc-linux-gnu-library/3.6’
(as ‘lib’ is unspecified)
trying URL 'http://package-proxy/src/contrib/sf_0.9-2.tar.gz'
Content type 'application/x-tar' length 11180611 bytes (10.7 MB)
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downloaded 10.7 MB

* installing *binary* package ‘sf’ ...
* DONE (sf)

The downloaded source packages are in
    ‘/tmp/RtmpGfWAoA/downloaded_packages’

install.packages("rworldmap")

Error in install.packages : Updating loaded packages

library(tidyverse)
library(ggplot2)
library(plotly)
library(gganimate)
library(rworldmap)

#worldhap–> #world2020–>

world2020

worldhap

#Interactive Geom_col 1(Region:Happiness)

m<-ggplot(data= world2020,
mapping= aes(x = world2020$"Regional indicator" , y= world2020$"Ladder score", fill= world2020$"Country name"))+
geom_col() +
theme(axis.text.x = element_text (angle = 90), legend.position = "none")+
labs(x= "Region",y= "Happiness Ranking",
title = "Rankings for Every Region's Happiness Level",
subtitle="Year: 2020",
caption="Source: Kaggle- World Happiness Report 2020")

plot(m)

#Interactive Geom_col 2 (Economic:Happiness)

ggplot(data = world2020, aes(x = world2020$"Logged GDP per capita" , y = world2020$"Ladder score", fill = world2020$"Regional indicator"))+
geom_point(shape = 1)+
geom_smooth(method= lm, color = "pink")+
theme(axis.text.x = element_text (angle = 30))+
labs(title="Country's Economy vs. Their Happiness",
x= "GDP Per Capita",
y= "Happiness Score",
caption= "Source:Kaggle- World Happiness Report 2020")

ggplotly(p)

`geom_smooth()` using formula 'y ~ x'

#Interactive Graph 3 (Corruption: Life Expectancy)

p <-ggplot(data= world2020,
mapping= aes(x = world2020$"Regional indicator", y= world2020$"Perceptions of corruption", color = world2020$"Healthy life expectancy"))
p + geom_col() + guides(fill = FALSE)+
theme(axis.text.x = element_text (angle = 90))+
labs(x= "Region",y= "Corruptive Outlook",
title = "Corruption's Effect on Average Life Expectancy",
subtitle="Year: 2020",
caption="Source: Kaggle")

NA

#Interactive Geom_point 4 (Freedom:Positive Effect) #Add smoothing

p <- ggplot(data = worldhap, aes(x = Freedom , y =worldhap$"Positive affect", fill = worldhap$"Country (region)"))+
geom_point(shape = 1)+
geom_smooth(size = 0.5, method = "loess", se = FALSE)+
theme(legend.position = "none")+
labs(title="Freedom's Positive Effect Worldwide",
x= "Freedom Score",
y= "Positive Affect",
caption= "Source:Kaggle- World Happiness")

ggplotly(p)

`geom_smooth()` using formula 'y ~ x'
Removed 1 rows containing non-finite values (stat_smooth).

#Interactive Geom_Point 5(Generosity:Happiness)

p <- ggplot(data = world2020, aes(x = Generosity , y =world2020$"Ladder score", fill = world2020$"Country name"))+
geom_point(shape = 1)+
geom_smooth(method= lm, color = "pink")+
theme(legend.position = "none")+
labs(title="Generosity's Effect on Happiness Worldwide",
x= "Generosity Score",
y= "Happiness Score",
caption= "Source:Kaggle- World Happiness Report 2020")

ggplotly(p)

`geom_smooth()` using formula 'y ~ x'

#GGAnimate Graph 1 (Social Support:Generosity)

ggplot(world2020, aes(Generosity, world2020$"Social support", colour = world2020$"Regional indicator" ))+
geom_point() +
transition_states(
world2020$"Regional indicator",
transition_length = 3,
state_length= 1)+
enter_fade()+
exit_shrink()+
ease_aes('sine-in-out')+
theme(axis.text.x = element_text (angle = 90))+
#Here comes the gganimate code
labs(title="Worldwide Social Support vs. Generosity",
subtitle= "By Region: {closest_state}",
x= " Region's Generosity Score",
y= "Region's Social Support Rate",
caption= "Source: Kaggle- World Happiness 2020")

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Finalizing encoding... done!

#GGAnimate Graph 2 (Discussion Point Plot)

ggplot(world2020, aes(world2020$"Ladder score", world2020$"Healthy life expectancy", colour = world2020$"Regional indicator"))+
geom_point(alpha = 0.5)+
transition_states(world2020$"Regional indicator",
transition_length = 2,
state_length= 1)+
scale_size(range = c(2,15))+
scale_x_log10() +
facet_wrap(~world2020$"Regional indicator")+
labs(title="World Happiness vs. Worldly Life Expectancy",
subtitle= "By Region: {closest_state}",
x= "Happiness Score",
y= "Life Expectancy",
caption= "Source: Kaggle- World Happiness 2020")

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Finalizing encoding... done!

#Map 1 (Top 10 Happiest Countries)

library(maptools)

Checking rgeos availability: FALSE
    Note: when rgeos is not available, polygon geometry     computations in maptools depend on gpclib,
    which has a restricted licence. It is disabled by default;
    to enable gpclib, type gpclibPermit()

    data=(wrld_simpl)
    myCountries = wrld_simpl@data$NAME %in% c("Finland", "Denmark", "Norway", "Iceland", "Sweden", "Netherlands", "New Zealand", "Switzerland", "Austria", "Canada")
    plot(wrld_simpl, col = c(gray(.80), "yellow")[myCountries+1])

#Map 2 (Finland- Happiest Place on Earth)

library(maptools)
    data=(wrld_simpl)
    myCountries = wrld_simpl@data$NAME %in% c("Finland")+
        #choose the desired nation(s) you would like to highlight
    plot(wrld_simpl, col = c(gray(.80), "yellow")[myCountries+1])

#Plot 10! World Happiness Pie Chart

library(plotly)
pie <- plot_ly(world2020, labels = ~world2020$"Regional indicator",  type = 'pie')
pie <- pie %>% layout(title = 'World Happiness Composition',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

pie